In recent years, with the development of an industry, need for an automobile, a freight vehicle, and a high-speed train has further increased and damage has occurred due to a lot of vehicular accidents caused by problems such as an increase in the number of vehicles and a high-speed of the vehicle. Such vehicular accidents have become more severe, and as a result, the yearly physical and human tolls due to the vehicular accidents have been compared to causalities of war.
The accident may be caused by a driver's mistake but are often caused by a vehicle defect. Therefore, advanced countries have gradually recognized the necessity of national management for these vehicles.
Therefore, the defect, which may occur in the vehicle, is determined in advance by sensing main devices installed in the vehicle in order to more rapidly cope with the existence of the defects in the vehicles and transmitting the information to the driving control center, which manages the devices, to prevent an accident which may occur from a defective vehicle.
However, in the case where data measured at a predetermined cycle is transmitted to the driving control center so as to monitor a plurality of main devices of multiple running vehicles in near real time, a capacity of data to be transmitted and processed is vast, and as a result, a large bandwidth of a network is required and a lot of resources are required to store the transmitted data. Therefore, reduction of the data transmitted from the vehicle is requested to access data in near real time or whenever necessary.
In a method of compressing massive data, a conventional audio compression technology that compresses audio data is lossy compression of deleting a sound band that humans cannot hear, a JPEG compression method of compressing a still image is compression of a color still image by deleting information which is duplicated on a screen, a GIF compression method is non-lossy compression, but can express only 256 colors and thus has a limit in the amount of data, and MPEG for compressing a moving picture can compress a lot of data.
However, the data compression method as lossy compression of deleting voice or image information, which is less acoustically or visually sensitive, is accompanied with loss of information and even in the case of the non-lossy compression, a complicated calculation process for compression and restoration is requested, and as a result, a lot of resources such as a high-speed processor and massive memories are requested.
In particular, in a defect monitoring system that monitors whether a main device of the running vehicle is defective, data indicating state information of each device in the vehicle is much less than image data in terms of a bit number, but a processor in an embedded system processing information from various sensors used for monitoring in the vehicle is not capable of performing a calculation for the compression, and as a result, a delay inevitably occurs in some cases.
This may be a large problem due to the delay of the data in the defect monitoring system that monitors the defect in near real time so as to rapidly cope with even a small defect of the main device in the vehicle and if the compressed data is not restored to its original data, the compressed data cannot be immediately used and the data cannot be compressed before a predetermined amount of data is collected, and as a result, in some cases, it is not appropriate to use compression when the data needs to be transmitted immediately.
Further, data transmitted from each device of the vehicle requires accuracy in terms of information, and as a result, there is a problem that lossy compression occurs in the data compression technology even though non-lossy compression is presumed.